# Is ChatGPT More Empathetic than Humans?

arXiv: https://arxiv.org/abs/2403.05572 · HTML: https://mlsanity.com/paper/2403.05572/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements](https://mlsanity.com/paper/2310.05140/) (2023)
- [Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy](https://mlsanity.com/paper/2305.10195/) (2023)
- [Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation](https://mlsanity.com/paper/2308.00085/) (2023)

## Built on by (most cited first, 8 of 8)

- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue](https://mlsanity.com/paper/2601.19922/) (2026)
- [Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries](https://mlsanity.com/paper/2504.09271/) (2025)
- [Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues](https://mlsanity.com/paper/2412.20264/) (2024)
- [The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills](https://mlsanity.com/paper/2409.14050/) (2024)
- [Personality Editing for Language Models through Adjusting Self-Referential Queries](https://mlsanity.com/paper/2502.11789/) (2025)
- [Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization](https://mlsanity.com/paper/2509.04646/) (2025)
- [Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts](https://mlsanity.com/paper/2605.06476/) (2026)

## Abstract (arXiv)

This paper investigates the empathetic responding capabilities of ChatGPT, particularly its latest iteration, GPT-4, in comparison to human-generated responses to a wide range of emotional scenarios, both positive and negative. We employ a rigorous evaluation methodology, involving a between-groups study with 600 participants, to evaluate the level of empathy in responses generated by humans and ChatGPT. ChatGPT is prompted in two distinct ways: a standard approach and one explicitly detailing empathy's cognitive, affective, and compassionate counterparts. Our findings indicate that the average empathy rating of responses generated by ChatGPT exceeds those crafted by humans by approximately 10%. Additionally, instructing ChatGPT to incorporate a clear understanding of empathy in its responses makes the responses align approximately 5 times more closely with the expectations of individuals possessing a high degree of empathy, compared to human responses. The proposed evaluation framework serves as a scalable and adaptable framework to assess the empathetic capabilities of newer and updated versions of large language models, eliminating the need to replicate the current study's results in future research.